Diagnostic tests have been provided for detecting, screening, monitoring, and/or predicting the future development of various health states (e.g., disease states) in a subject. Typically, the detecting, screening, monitoring, or prognosis is provided by a diagnostic test based, at least in part, on the level(s) of one or more biological markers (“biomarkers”) in a clinical sample taken from the subject (e.g., the subject's blood), or the presence thereof. Such biomarkers are selected because the presence, absences, or levels of such biomarkers alone or in combination are indicative of the presence, stage, or future clinical course of the health state. Often times, but not necessarily, the diagnostic test may additionally be based on clinical information concerning the subject. Determining an appropriate diagnosis or prognosis for a subject can, for example, advantageously increase the subject's chances for survival and/or recovery.
Diagnostic tests must undergo a development stage during which the tests are formulated (and optionally tested/validated) using previously collected samples stored for future research and development needs. This process is prior to their use in diagnosing or predicting the development of disease in subjects in real time. The information used to formulate and validate the tests typically comes from clinical samples for a cohort of subjects for whom at least some biochemical and clinical data is known regarding the presence or absence of the health state under consideration. Thus, traditionally a party who is desirous of developing a diagnostic test for a given health state is required to commit significant resources to the collection of clinical samples (and optionally clinical information such as medical history) from subjects who have, and/or lack, the health state, often at various stages. This data collection process can take many years, depending on the type of disease being considered and the party's relative access to suitable subjects.
Traditional approaches for developing diagnostic tests also require the clinical samples that are collected to have sufficiently large volumes, and such large samples cannot always be readily obtained. Specifically, traditional biomolecular detection approaches require large sample volumes in order to allow for the selection of a set of biomarkers that will be useful in the determination of a patient's health state. Of all the biomarkers that are evaluated (e.g., 1-3, 150-300 biomarkers, or 1000 or more), only those biomarkers that are determined to aid in the determination of the health state in a patient are included in the final diagnostic test. For example, according to one approach, single-biomarker multiple ELISAs used to measure the presence or level of 300 biomarkers typically require a serum or plasma sample size of about 30 mL of specimen per individual (i.e., 100 uL per assay times 300 biomarkers). The required sample volume becomes 90 mL of specimen per individual if the assays are done in triplicate. This is a very large volume and is very impractical. In addition, few studies have ever been conducted where so much clinical sample was collected. Multiplexing, which involves measuring multiple biomarkers in the same reaction vessel, can reduce the overall required sample volume by way of conservation but requires compatibility between all the assay components and typically compromises sensitivity through increased background effects. As a result, on an assay by assay basis, individual assays are typically 10 or more fold more sensitive than their counterpart within a multiplexed assay.
In view of the foregoing, it would be desirable to provide systems and methods for developing diagnostic tests in which access to suitable clinical samples is improved and which rely on smaller sample volumes.